Many guidance, control, and navigation (GNC) applications provide precise navigation when necessary. For example, precision landing of a military aircraft or spacecraft requires precise navigation. In addition, unmanned vehicles, such as unmanned aerial vehicles (UAV) also require accurate position and velocity information in order to properly navigate an area (target). Most of these GNC applications employ one or more global positioning system (GPS) sensors to achieve a necessary level of navigation precision. In addition, stringent requirements on precision landing and navigation dictate stringent performance requirements for the GNC application.
Various methods and systems have been employed to meet these stringent performance requirements. Interest point (corner) detection extracts certain types of point features and estimates contents of the target image. Corner detection is frequently used in navigation, motion detection, and image tracking and recognition. An interest point is a point in the image which has a well-defined position that is suitable for detection, such as the intersection of two edges (that is, a corner). Corner detection quality is typically based on detecting the same corner in multiple images, which are similar but not identical (for example, multiple images having different lighting, translation, and rotation). Simple approaches to corner detection of multiple images often become very computationally expensive and generate less than adequate results.
An inertial measurement unit (IMU) measures acceleration in a plurality of directions. The IMU uses the measured accelerations to estimate motion in each of the measured directions. Unfortunately, the IMU measurements are subject to measurement drift that adversely affects the accuracy of the IMU measurements. Available GPS satellite data updates the IMU measurements, improves the accuracy of the position and velocity data, and periodically corrects any accuracy errors in the IMU measurements. When the GPS satellite data is not available, however, the motion estimates are not available in real time. Furthermore, when results are not immediately available, these stringent performance requirements are violated.